Introduction

SBA - Small Business Profiles for the States and Territories

The Office of Advocacyā€™s Small Business Profiles are an annual analysis of each stateā€™s small business activities. Each profile gathers the latest information from key federal data-gathering agencies to provide a snapshot of small business health and economic activity. This yearā€™s profiles report on state economic growth and employment; small business employment, industry composition, and turnover; plus business owner demographics and county-level employment change.

https://www.sba.gov/

In [1]:
from IPython.core.display import display, HTML
display(HTML("""<style> .container {width:96% !important;}</style>"""))

from IPython.display import IFrame
In [2]:
import pandas as pd
import multiprocessing
import numpy as np
from multiprocessing.dummy import Pool as ThreadPool
from functools import partial
import math

# Handle s3 or local
import s3fs
from os import listdir
from os.path import isfile, join
import subprocess

# Analysis
from plotly.offline import init_notebook_mode, iplot
import cufflinks as cf
init_notebook_mode()
cf.go_offline()

Path to the files

In [3]:
import sys
sys.path.insert(0,'../')
from Tools.paths import *
In [4]:
def list_files(path,ext = 'pdf'):
    if path.startswith('s3://'):  
        onlyfiles = subprocess.check_output(['aws', 's3', 'ls', path_s3])
        onlyfiles = onlyfiles.split('\n')
        onlyfiles = [f.split(" ")[-1] for f in onlyfiles]
    else:
        onlyfiles = [f for f in listdir(path_local) if isfile(join(path_local, f))]
    onlyfiles = [f for f in onlyfiles if f.endswith('.{}'.format(ext))]
    files = [f.replace('.{}'.format(ext),'') for f in onlyfiles]
    return files
In [5]:
def path(path,name,ext = 'pdf'):
    path_file = '{}{}.{}'.format(path,name,ext)
    return path_file

Analysis

Industry

In [6]:
industry = pd.read_csv(path(path_s3_out,'industry','csv'),sep = ";")
In [7]:
industry = industry[industry['Industry']!='Total']
In [8]:
industry.head()
Out[8]:
Industry 1-499 Employees 1-19 Employees Nonemployer Firms Total Small Firms State
0 Retail Trade 10674 9627 27992 38666 Alabama.pdf
1 Other Services (except Public Administration) 10042 9332 63575 73617 Alabama.pdf
2 Professional, Scientiļ¬c, and Technical Services 8081 7378 31099 39180 Alabama.pdf
3 Health Care and Social Assistance 7823 6670 21808 29631 Alabama.pdf
4 Construction 7143 6373 39463 46606 Alabama.pdf
In [9]:
df_aux = industry[['Industry','1-499 Employees','Nonemployer Firms']] 
df_aux = df_aux.groupby('Industry').sum()
In [10]:
df_aux.iplot(kind = 'bar')

Employment

In [11]:
employment = pd.read_csv(path(path_s3_out,'employment','csv'),sep = ";")
In [12]:
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')
df.head()
df = df[['code','state']]
df.drop_duplicates(inplace=True)
In [13]:
employment.head()
Out[13]:
Industry Small Business Employment Total Private Employment Small Business Emp Share State
0 Health Care and Social Assistance 113580.0 240549.0 47.2 Alabama.pdf
1 Accommodation and Food Services 89707.0 161421.0 55.6 Alabama.pdf
2 Retail Trade 87257.0 222277.0 39.3 Alabama.pdf
3 Manufacturing 79632.0 242093.0 32.9 Alabama.pdf
4 Other Services (except Public Administration) 68770.0 80073.0 85.9 Alabama.pdf
In [14]:
df_aux = employment.groupby('State').sum()
In [15]:
df_aux = df_aux.reset_index()
In [16]:
df_aux['State'] = df_aux.State.apply(lambda x: x.replace("_"," ").split(".")[0])
print df_aux.shape
df_aux = df_aux.merge(df, left_on = 'State', right_on = 'state', how = 'left')
print df_aux.shape

(51, 4)
(51, 6)
In [17]:
scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
            [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]

df_aux['text'] = df_aux['State']

data = [ dict(
        type='choropleth',
        colorscale = scl,
        autocolorscale = False,
        locations = df_aux['code'],
        z = df_aux['Small Business Employment'].astype(float),
        locationmode = 'USA-states',
        text = df_aux['text'],
        marker = dict(
            line = dict (
                color = 'rgb(255,255,255)',
                width = 2
            ) ),
        colorbar = dict(
            title = "Millions USD")
        ) ]

layout = dict(
        title = '2016 USA Small Business Employment by State',
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showlakes = True,
            lakecolor = 'rgb(255, 255, 255)'),
             )
    
fig = dict( data=data, layout=layout )
iplot( fig, filename='d3-cloropleth-map' )

Demographics

In [18]:
demographic = pd.read_csv(path(path_s3_out,'demographic','csv'),sep = ";")
In [19]:
demographic.head()
Out[19]:
Alaskan-owned American-owned Asian-owned Hispanic-owned Islander-owned Minority-owned Nonminority-owned State
0 27.0 28.7 35.4 51.5 -16.9 30.7 -8.6 Alabama.pdf
1 9.3 22.2 41.2 NaN 32.8 16.5 -2.3 Alaska.pdf
2 20.2 52.8 35.2 69.7 NaN 58.8 -7.3 Arizona.pdf
3 15.7 55.3 43.1 46.8 66.3 52.2 -7.4 Arkansas.pdf
4 27.8 33.9 28.6 51.5 21.5 43.9 -2.8 Colorado.pdf
In [20]:
df_aux = demographic
In [21]:
df_aux['State'] = df_aux.State.apply(lambda x: x.replace("_"," ").split(".")[0])
print df_aux.shape
df_aux = df_aux.merge(df, left_on = 'State', right_on = 'state', how = 'left')
print df_aux.shape

(51, 8)
(51, 10)
In [22]:
scl = [[0.0, 'rgb(204,229,255)'],[0.2, 'rgb(153,204,255)'],[0.4, 'rgb(102,178,255)'],\
            [0.6, 'rgb(51,153,255)'],[0.8, 'rgb(0,128,255)'],[1.0, 'rgb(0,102,204)']]

df_aux['text'] = df_aux['State']

data = [ dict(
        type='choropleth',
        colorscale = scl,
        autocolorscale = False,
        locations = df_aux['code'],
        z = df_aux['Nonminority-owned'].astype(float),
        locationmode = 'USA-states',
        text = df_aux['text'],
        marker = dict(
            line = dict (
                color = 'rgb(255,255,255)',
                width = 2
            ) ),
        colorbar = dict(
            title = "%")
        ) ]

layout = dict(
        title = '2016 USA Non-minority changes in business ownership by State',
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showlakes = True,
            lakecolor = 'rgb(255, 255, 255)'),
             )
    
fig = dict( data=data, layout=layout )
iplot( fig, filename='d3-cloropleth-map' )